Systems and methods for service allocation based on real-time service provider and requestor attributes

ABSTRACT

A system described herein may provide a technique for identifying states associated with service providers based on biometric, sensor, and/or other information associated with a set of service providers. A request for service may be received, and a particular service provider may be selected based on a particular state associated with the particular service provider, as determined based on the biometric, sensor, and/or other information associated with the particular service provider. State information associated with a requestor of the service may be identified and used as a factor in selecting the particular service provider to respond to the service request.

BACKGROUND

Service providers may provide services to requestors, such as technicalsupport services, providing product information, responding toinquiries, and/or other services. Such services may be provided viavoice communications such as telephone calls, written chatcommunications, or the like.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example determination of respective states of aset of service providers (referred to herein as “providers”) based oninformation associated with the providers, in accordance with someembodiments;

FIG. 2 illustrates an example determination of a particular stateassociated with a particular provider based on comparing providerinformation associated with the particular provider to a set ofcandidate provider state models, in accordance with some embodiments;

FIG. 3 illustrates an example determination of respective states of aset of service requestors (referred to herein as “requestors”) based oninformation associated with the requestors, in accordance with someembodiments;

FIG. 4 illustrates an example determination of a particular stateassociated with a particular requestor based on comparing providerinformation associated with the particular requestor to a set ofcandidate requestor state models, in accordance with some embodiments;

FIG. 5 illustrates an example correlation between respective providerstate models and requestor state models, in accordance with someembodiments;

FIG. 6 illustrates an example assignment of respective providers tohandle requests from a set of requestors based on provider and/orrequestor models identified with respect to a set of candidate providersand the set of requestors, in accordance with some embodiments;

FIG. 7 illustrates an example process for assigning a provider to handlea service request from a particular requestor, in accordance with someembodiments;

FIG. 8 illustrates an example environment in which one or moreembodiments, described herein, may be implemented;

FIG. 9 illustrates an example arrangement of a radio access network(“RAN”), in accordance with some embodiments; and

FIG. 10 illustrates example components of one or more devices, inaccordance with one or more embodiments described herein.

DETAILED DESCRIPTION OF EXAMPLE EMBODIMENTS

The following detailed description refers to the accompanying drawings.The same reference numbers in different drawings may identify the sameor similar elements.

Embodiments described herein provide for the use of artificialintelligence/machine learning (“AI/ML”) techniques or other suitabletechniques to assign service providers (referred to herein simply as“providers”) to handle requests from service requestors (referred toherein as “requestors”). A “provider,” as referred to herein, mayinclude any type of provider of a suitable type of requested service.For example, providers may include service representatives, operators,and/or other types of providers that handle support requests,information requests, and/or requests for other types of service from arequestor. In some embodiments, such providers may be associated with acall center or other facility that provides services via voicecommunications (e.g., telephone calls or other types of voice calls),video communications (e.g., video calls, videoconferences, etc.),text-based communications, or other types of communications. Similarly,a requestor may request service from the provider by placing a requestvia a telephone call, a web page, a web portal, an application (or“app”), and/or in some other suitable manner.

In accordance with embodiments described herein, requests may beprovided to an appropriate provider, out of a set of candidateproviders, based on information associated with the candidate providers,the requestor, the requested service, and/or other factors. As describedherein, provider information (e.g., based on which an appropriateprovider may be selected) for a given provider may include biometric,vital, and/or sensor information related to the provider, such as brainwave information, speech patterns, pulse (e.g., heart rate), bloodpressure, temperature, a measure of caloric burn and/or metabolic rate,sleep patterns, facial expressions, and/or other suitable types ofinformation. Such provider information may be monitored and/or collectedby wearable devices or sensors worn by a given provider, such as “smart”headphones, “smart” glasses, a “smart” watch, a fitness tracker, one ormore medical devices or sensors, or other apparatus that is capable ofcollecting one or more of the above-mentioned types of information. Insome embodiments, provider information may be monitored and/or collectedby another type of device or system, such as a microphone, a camera, asmartphone, and/or some other type of device or system that includessensors and/or other suitable functionality to determine suchinformation. Further, requestor information (e.g., based on which anappropriate provider may be selected) for a given requestor may includerequest type (e.g., a type or amount of service being requested),request frequency (e.g., how often the given requestor requestsservice), speech patterns, text-based communication patterns, and/orother information associated with the requestor.

Generally, for example, provider information may be used to determine astate associated with a given provider, which may indicate theprovider's effectiveness when handling particular types of requestsand/or particular requestors at a particular time. For example, based ona given provider's brain wave information (e.g., based on alpha, beta,gamma, delta, and/or theta waves), sleep patterns, blood pressure, andspeech patterns, a system of some embodiments may determine that theprovider is in an alert and/or anxious state, and may avoid selectingthis provider for service request with a relatively difficult to handleservice type and/or a requestor whose speaking patterns indicate thatthe requestor may be upset or angry. Instead, the given provider may bematched with another, less stress-inducing service request, and/or thegiven provider may be instructed to rest, take a break, end his or hershift, or the like. For example, in some embodiments, remedial actionssuch as notifying the provider to rest, take a break, etc. may beidentified based on a determined provider state, without considerationto requests or requestors. Accordingly, provider productivity and/orperformance may be enhanced. Similarly, in embodiments where providersare matched to requests from requestors, the user experience forrequestors may be enhanced by virtue of determining a provider whosestate is best suited for a particular request or requestor state.

As shown in FIG. 1 , for example, AI/ML Provider Selection System(“APSS”) 101 may monitor and/or receive (at 102) provider informationassociated with a set of providers 103-1 through 103-X (sometimesreferred to individually herein as “provider 103” or collectively as“providers 103”). For example, as similarly discussed above, suchprovider information may include biometric information, sensorinformation, or the like associated with each respective provider 103.For example, provider 103-1 may be associated with a first “smart”headset that measures brain wave activity associated with provider103-1, while provider 103-2 may be associated with a second “smart”headset that measures brain wave activity associated with provider103-2. As another example, APSS 101 may receive video informationdepicting provider 103-1, provider 103-2, and/or provider 103-X, and mayperform a suitable visual analysis (e.g., computer vision analysis,pattern matching analysis, and/or other suitable image or visualanalysis) to determine facial expressions associated with provider103-1, provider 103-2, and provider 103-X.

As further shown, APSS 101 may identify (at 104) a respective providerstate 105 for each provider 103. For example, APSS 101 may determinethat provider 103-1 is associated with provider state 105-1, thatprovider 103-2 is associated with provider state 105-2, and thatprovider 103-X is associated with provider state 105-X. Thus, as furtherdescribed below, the identification of a respective provider state 105for a given provider 103 may be based on analyzing some or all of thereceived (at 102) provider information, and comparing the providerinformation to one or more models.

While the figure depicts provider information as being monitored and/orreceived “from” respective providers 103, in practice, APSS 101 mayreceive such information from devices or systems not explicitly shown inFIG. 1 . For example, APSS 101 may receive provider information from aUser Equipment (“UE”) such as a mobile telephone, an Internet of Things(“IoT”) device, a workstation, a tablet computer, a web server, and/orsome other type of device or system. Such UE or other device may includeand/or may be communicatively coupled to one or more sensors (e.g.,brain wave sensors, heartbeat monitors, blood pressure monitors, etc.)or other suitable types of devices or components that are capable ofcapturing or otherwise determining provider information discussedherein.

For example, as shown in FIG. 2 , one or more UEs 201 may determineand/or receive (at 202) biometric information, sensor information,and/or information associated with provider 103. While the example ofFIG. 2 is shown in the context of such information being determined orreceived from one or more UEs 201, in practice, some embodiments maydetermine and/or receive such information from one or more other typesof devices or systems. Further, while FIG. 2 is shown in the context ofone or more UEs 201 (denoted in the figure as “UE(s) 201”), thedescription below provided examples in the context of one UE 201 for thesake of clarity. Additionally, as noted above, UEs 201 may includeand/or may be communicatively coupled to one or more sensors or othertypes of components that are capable of generating, receiving,determining, etc. the types of provider information described herein.

As shown, UE 201 may determine and/or receive brain wave information203, speech pattern information 205, facial expression information 207,and pulse and/or blood pressure information 209. In practice, UE 201 maydetermine and/or receive other suitable types of information in additionto, or in lieu of, brain wave information 203, speech patterninformation 205, facial expression information 207, and pulse and/orblood pressure information 209. For example, UE 201 may include and/ormay be communicatively coupled to an Electroencephalography (“EEG”)device or other suitable device that is capable of detecting brain waveinformation 203 associated with a given user (e.g., provider 103). Insome embodiments, UE 201 may include and/or may be communicativelycoupled to a wearable “smart” device, such as a set of “smart”headphones, “smart” glasses, or the like, that include an EEG device orother suitable device or sensor. Generally, brain waves may bemeasurable in different frequencies or bands, sometimes referred to asalpha, beta, gamma, delta, and theta. Different measurements (e.g.,amplitudes) across these varying frequencies or bands may correspond tolevels of happiness, stress, anger, frustration, or other emotions ormoods of a user.

Speech pattern information 205 may include the content of speech and/ortext-based communications from provider 103 (e.g., via UE 201, such asthrough a voice- or video-based call or a text-based messagingcommunication), which may include a transcribed version of speech fromprovider 103. For example, UE 201 and/or APSS 101 may use speech-to-texttechniques in order to generate or determine speech pattern information205. Additionally, or alternatively, speech pattern information 205 maybe based on the tone, volume, pitch, and/or other audible features(e.g., non-verbal features) of speech from provider 103 (e.g., via UE201). For example, generally, relatively loud (e.g., having a decibelrating or other measure of loudness above a threshold) utterances fromprovider 103 may indicate that provider 103 is upset or frustrated,while utterances that are within or “normal” range of volume associatedwith provider 103 may indicate that provider 103 is not upset orfrustrated, or that provider 103 is content or happy. As anotherexample, relatively fast (e.g., greater than a threshold quantity ofwords per second, minute, etc.) speech may indicate that provider 103 isexcited, relatively slow speech may indicate that provider 103 is tiredor confused, and speech within a “normal” or average range associatedwith provider 103 may indicate that provider 103 is not excited, tired,or confused. In some embodiments, UE 201 may include, and/or may becommunicatively coupled to, a microphone or other device that is capableof capturing audible and/or written speech from provider 103.

Facial expression information 207 may include images or video of a faceof provider 103. For example, UE 201 may include and/or may becommunicatively coupled to a camera or other device that is capable ofcapturing images and/or video associated with provider 103. For example,such camera may be situated or affixed at a workstation associated withprovider 103. Generally, when facial expression information 207 includesimages or video that depict a scowling face of provider 103, facialexpression information 207 may indicate that provider 103 is upset orangry. As another example, when facial expression information 207includes images or video that depict a smiling face of provider 103,facial expression information 207 may indicate that provider 103 ishappy. As discussed above, facial expression information 207 may bedetermined via computer vision analysis, pattern matching analysis,AI-based imagine analysis, and/or other suitable image or visualanalysis).

Pulse and/or blood pressure information 209 may include data (e.g.,time-keyed data points, graphs, or other suitable representation) of apulse, blood pressure, or other vital data associated with provider 103.For example, UE 101 may include, or may be communicatively coupled, anelectrocardiogram (“ECG”), a blood pressure monitor, a blood oxygensensor, or other suitable device, sensor, component, etc. that collectsand/or measures vital information associated with provider 103.

Although example types of information (e.g., biometric and/or sensorinformation) associated with provider 103, are discussed above, inpractice, other types of biometric and/or sensor information may be usedin the manner described herein. For example, sleep patterns associatedwith particular providers may be determined by UEs associated withrespective providers, such as mobile telephones, fitness trackers, orother suitable devices or systems. Sleep patterns may include, forexample, duration of sleep, amount of movement while sleeping, breathingpatterns while sleeping, and/or other suitable information. Further,while example types of sensors are discussed above, in practice, othertypes of sensors, components, devices, etc. may be used to determine orgenerate provider information.

UE 201 may output (at 204) provider information to APSS 101. Forexample, UE 201 may communicate with APSS 101 via an applicationprogramming interface (“API”), a web portal, or some other suitablecommunication pathway. UE 201 may output the provider information on anongoing basis, such as a periodic or intermittent basis, such that APSS101 is able to monitor (e.g., at 102) provider information on an ongoingbasis and determine (at 206) provider state information for eachprovider 103 based on up-to-date information. For example, as the moodor effectiveness of provider 103 changes, some or all of the monitoredprovider information (e.g., brain wave information 203, speech patterninformation 205, etc.) may change, and the corresponding provider statemodel 211 may change. For example, in the example of FIG. 2 , APSS 101may determine (at 206) that provider 103 is associated with particularprovider state model 211-2 (e.g., may select provider state model 211-2out of a set of candidate provider state models 211) based on theprovider information over a given time period. During another timeperiod, APSS 101 may receive a different set of provider information(e.g., different sensor readings, biometric information, etc.) forprovider 103 and may determine based on such changed information thatprovider 103 is associated with a different provider state model 211.For example, one provider state model 211 may relate to a “happy” state,another provider state model 211 may relate to a “tired” state, etc. Assuch, the respective provider state model 211 for a given provider 103may change over time, as the provider's mood and/or other attributeschange.

In order to determine a particular provider state model 211 for provider103 based on the received provider information, APSS 101 may use AI/MLtechniques (e.g., K-means clustering, neural networks, reinforced and/orunreinforced learning, and/or other AI/ML techniques) to correlateprovider information to a particular provider state model 211.Generally, in the example of FIG. 2 , brain wave information 203associated with provider 103 may match (e.g., may exactly match, mayhave at least a threshold measure of similarity, etc.) brain waveinformation associated with provider state model 211-2. As anotherexample, again in the context of FIG. 2 , speech pattern information 205associated with provider 103 may match speech pattern informationassociated with provider state model 211-2, and/or may more closelymatch the speech pattern information associated with provider statemodel 211-2 than speech pattern information associated with providerstate model 211-1 or provider state model 211-Y.

In some embodiments, a “match” may refer to an exact matching betweenmonitored provider information and information associated with a givenmodel. Additionally, or alternatively, a “match” may refer to a suitablemeasure of similarity (e.g., using a similarity or correlation analysis)between some or all of the monitored provider information andinformation associated with a given model.

While the selection (at 206) of a particular provider state model 211-2for provider 103 is described as being based on provider information(e.g., brain wave information 203, speech pattern information 205,facial expression information 207, pulse and/or blood pressureinformation 209, and/or other biometric and/or sensor information), inpractice, APSS 101 may select a particular provider state model 211 forprovider 103 based on one or more other types of information. Forexample, APSS 101 may make such selection based on factors such as howlong provider 103 has been providing service before a given request forservice is received (e.g., if provider 103 has been working a relativelylong shift), a level of experience or expertise of provider 103 in agiven field or specialty (e.g., as such field or specialty relates tothe request), a history of the provider 103 with a particular requestor(e.g., which may indicate that provider 103 and the particular requestorhave interacted in the past), and/or other factors.

In addition to identifying provider states (e.g., as discussed abovewith respect to FIGS. 1 and 2 ), APSS 101 may identify requestor states.As discussed below, APSS 101 may select a particular provider 103 tohandle a service request from a given requestor 303 based on identifiedprovider and requestor states, in order to maintain or enhance the userexperience for both providers 103 and requestors 303.

As shown in FIG. 3 , for example, APSS 101 may monitor and/or receive(at 302) requestor information associated with one or more requestorswho have requested a service. In some embodiments, such requests mayinclude contacting a call center, support messaging system, and/or otherdevice or system for which services are provided by providers 103. Assimilarly described above with respect to providers 103, and asdiscussed in more detail below, APSS 101 may identify (at 304)respective requestor states associated with requestors 303. For example,APSS 101 may determine that requestor 303-1 is associated with requestorstate 305-1, requestor 303-2 is associated with requestor state 305-2,and requestor 303-Z is associated with requestor state 305-Z.

As shown in FIG. 4 , for example, one or more UEs 201 may determineand/or otherwise receive (at 402) requestor information associated withrequestor 303. Additionally, or alternatively, APSS 101 may determinerequestor information based on information provided by UE 201, requestor303, and/or some other source. Requestor information may include, forexample, request type information 403, speech pattern information 405,and request history information 407 and/or one or more other types ofinformation in addition to, or in lieu of, request type information 403,speech pattern information 405, and request history information 407.

While the same reference numeral (i.e., 201) is used in FIGS. 2 and 4for UEs, the UE(s) 201 of FIG. 4 may be a different UE or set of UEsthan the UE(s) of FIG. 2 . For example, UEs 201 of FIG. 4 may bedifferent types of UEs, and/or may be different instances of the sametype of UE as UEs 201 of FIG. 2 .

Request type information 403 may indicate a type of request or service.For example, request type information 403 may indicate that requestor303 is requesting technical support with a particular device (e.g., aparticular make or model of telephone, television, laptop computer, orother type of device), may indicate that requestor 303 has an inquiryregarding upgrading a level of service (e.g., Internet service, wirelesstelephone service, etc.), or some other type of service. Such requesttypes 403 may correspond to, for example, specialties or skills withwhich one or more providers 103 are associated. For example, oneparticular provider 103 may be associated with a “mobile telephonetechnical support” skill, while another particular provider may beassociated with a “television technical support” skill.

Speech pattern information 405 may include information similar to thatdescribed above with respect to 205. For example, speech patterninformation 405 may include the content and/or audible attributes (e.g.,tone, pitch, volume, etc.) of speech or text-based communications fromrequestor 303 (e.g., via UE 201).

Request history information 407 may include information regarding pastservice requests associated with requestor 303. For example, requesthistory information 407 may indicate a time at which one or moreprevious service requests were received from requestor 303 (e.g., via UE201 and/or some other device or system), one or more types of servicepreviously requested, and/or other history information associated withrequestor 303. Such information may indicate, for example, whetherrequestor 303 is making repeat requests for the same type of service,which may indicate a level of dissatisfaction, frustration, or the like.

APSS 101 may receive (at 404) the requestor information from UE 201and/or some other devices or systems. In some embodiments, APSS 101 mayreceive the requestor information in conjunction with a request forservice from requestor 303. In some embodiments, APSS 101 may receivethe requestor information on some other basis, such as an ongoing,periodic, and/or intermittent basis.

APSS 101 may further determine (at 406) a particular requestor statemodel 411 associated with requestor 303. For example, APSS 101 mayselect requestor state model 411-1 out of a set of requestor statemodels 411 based on matching, correlating, etc. the requestorinformation to attributes or information associated with requestor statemodel 411-1. For example, as similarly noted above, APSS 101 may performa similarity analysis or other suitable technique to determine whichrequestor state model 411 matches the requestor information, and/orwhich particular requestor state model 411 of the candidate set ofrequestor state models 411 matches the requestor information.

As similarly noted above, APSS 101 may determine (at 406) a matchingrequestor state model 411 for requestor 303 in a dynamic fashion, suchthat the same requestor 303 may be associated with different requestorstate models 411 at different times. For example, requestor 303 may beassociated with a first requestor state model 411 when issuing a firstservice request, and may be associated with a different second requestorstate model 411 when issuing a second service request. For example,requestor 303 may initially request technical support for a particularmodel of smart phone, at which point APSS 101 may determine thatrequestor 303 is associated with a “mild” or “happy” state (e.g.,associated with a first requestor state model 411). Requestor 303 maylater request technical support for the same smart phone (e.g., asubsequent service request of the same type), and APSS 101 may furtherreceive information indicating that requestor 303 is associated withparticular speech pattern information 405, indicating louder and/orfaster speech patterns. Based on receiving this second request, APSS 101may determine that requestor 303 is associated with a “frustrated” or“angry” state (e.g., associated with a different second requestor statemodel 411).

As shown in FIG. 5 , APSS 101 may further determine (at 502)correlations between provider state models 211 and requestor statemodels 411. The correlation may include generating or determiningaffinities, scores, and/or other parameters or characteristics thatindicate that a particular provider state model 211 and a particularrequestor state model 411, and/or that indicate how strongly or closelyprovider state model 211 and requestor state model 411 are correlated.For example, APSS 101 may determine (at 502) that provider state model211-1 is relatively closely correlated to requestor state model 411-1(e.g., a correlation score of 99 on a scale of 1 to 100), that providerstate model 211-1 is less strongly correlated to requestor state model411-2 (e.g., a correlation score of 50 on a scale of 1 to 100), and thatprovider state model 211-1 is not strongly correlated to, or is not atall correlated to, requestor state model 411-3 (e.g., a correlationscore of 1 on a scale of 1 to 100).

As discussed herein, when receiving a request from a particularrequestor 303, APSS 101 may determine (e.g., at 406) a particularrequestor state model 411 associated with requestor 303, and/orassociated with the request from requestor 303. APSS 101 may furtheridentify a particular provider 103 to provide the requested servicebased on respective provider state models 211 associated with a set ofcandidate providers 103 (e.g., determined at 206). Specifically, forexample, APSS 101 may identify an appropriate provider 103 based oncorrelating provider state models 211, associated with the candidate setof providers 103, to the particular requestor state model 411 identifiedwith respect to requestor 303. For example, APSS 101 may select aparticular provider 103 which is associated with the particular providerstate model 211 that has a highest measure of correlation to requestorstate model 411 associated with requestor 303.

In some embodiments, APSS 101 may utilize one or more other factors inselecting provider 103 to service the request from requestor 303 (e.g.,in addition to the correlation between provider state model 211 andrequestor state model 411). For example, in some embodiments, suchfactors may result in APSS 101 selecting a particular provider 103 whichis associated with a provider state model 211 that has a lower measurecorrelation to requestor state model 411 than the highest correlationbetween provider state models 211 of candidate providers 103 andrequestor state model 411. For example, such factors may include aservice history between a particular provider 103 and a particularrequestor 303. For example, if requestor 303 has expresseddissatisfaction with services received from the particular provider 103and/or an interaction with provider 103, APSS 101 may select a differentprovider 103 to provide service in response to a service request fromrequestor 303, even if a provider state model 211 associated with theparticular provider 103 has a highest measure of correlation torequestor state model 411 associated with requestor 303. As anotherexample, requestor 303 may indicate a language preference that does notfall within a set of languages associated with (e.g., spoken by)provider 103, and APSS 101 may select a provider 103 based on languagepreference and based on correlating requestor state model 411 to aparticular provider state model 211 associated with the selectedprovider 103.

In some embodiments, APSS 101 may utilize one or more AI/ML techniquesto generate or refine the correlations between provider state models 211and requestor state models 411. In some embodiments, APSS 101 mayreceive a set of “training data” that indicates an initial set ofcorrelations between one or more provider state models 211 and one ormore requestor state models 411, may execute simulated interactionsbetween providers 103 (associated with provider state models 211) andrequestors 303 (associated with requestor state models 411), and may usefeedback from the simulated interactions to modify measures ofcorrelations between respective provider state models 211 and requestorstate models 411. For example, such feedback may include quantitativefeedback such as elapsed time to resolve a particular request, such as aduration of a telephone call, a duration of a text-based communicationsession, a time elapsed between the service request and a determinationthat a particular device or system that is the subject of the request isoperating within a set of operational parameters, or other quantitativefeedback. In some embodiments, the feedback may include qualitativefeedback, such as a rating provided by requestor 303, a survey responseprovided by requestor 303, or other qualitative feedback. In someembodiments, the refinement of correlations of provider state models 211to requestor state models 411, and/or the refinement of provider statemodels 211 and/or requestor state models 411 themselves, may beperformed based on feedback in live, “real world” scenarios in additionto, or in lieu of, simulated service requests.

FIG. 6 illustrates an example selection of a set of providers 103 toprovide service in response to a set of service requests from a set ofrequestors 303. For example, as shown, APSS 101 may receive (at 602) anindication that service requests have been received from requestor303-1, requestor 303-2, and requestor 303-2. As similarly discussedabove, APSS 101 may determine respective requestor states 305 forrequestors 303, and may determine respective provider states 105 forproviders 103. As also discussed above, requestor states 305 andprovider states 105 may include and/or may be otherwise based onrequestor state models 411 and provider state models 211, respectively.

Based on correlating requestor states 305 and provider states 105 (e.g.,based on measure of correlation between requestor state models 411 andprovider state models 211, as discussed above), APSS 101 may assign (at604) respective providers 103 to provide service to requestors 303. Forexample, APSS 101 may assign (at 604-1) provider 103-4 to provideservice in response to a request from requestor 303-1, may assign (at604-2) provider 103-2 to provide service in response to a request fromrequestor 303-2, and may assign (at 604-3) provider 103-2 to provideservice in response to a request from requestor 303-3.

In this example, the set of candidate providers 103 includes moreproviders 103 than pending requests from requestors 303. As such, fewerthan all of the candidate providers 103 may be selected to handle therequests. For example, as shown, provider 103-3 may be “idle” or notassigned. Provider 103-3 may thus be available for subsequent requests(e.g., from other requestors 303). In some embodiments, APSS 101 maydetermine that provider 103-3 should be idle, or not assigned to handleservice requests, for a particular period of time based on providerstate 105-3. For example, APSS 101 may determine that provider state105-3 is associated with a state of tiredness, frustration, or the like,and may accordingly determine that provider 103 should be idle for sometime (e.g., 5 minutes, 60 minutes, 4 hours, etc.). The idle timeassigned to provider 103-3 may result, for example, in the change ofstate from provider state 105-3 to another provider state 105 which isassociated with less tiredness, frustration, etc. After such change,provider 103-3 may become more likely to be selected to handle servicerequests.

FIG. 7 illustrates an example process 700 for assigning a provider 103to handle a service request from a particular requestor 303. In someembodiments, some or all of process 700 may be performed by APSS 101. Insome embodiments, one or more other devices may perform some or all ofprocess 700 in concert with, and/or in lieu of, APSS 101.

As shown, process 700 may include receiving, generating, and/or refining(at 702) provider state models 211, requestor state models 411, and/orcorrelations thereof. For example, as discussed above, provider statemodels 211 may include brain wave information 203, speech patterninformation 205, facial expression information 207, pulse and/or bloodpressure information 209, and/or other suitable factors based on whichthe state of a given provider 103 may be determined, classified,categorized, clustered, etc. For example, a first provider state model211 may be associated with a “happy” or “content” state, while a secondprovider state model 211 may be associated with a “frustrated” or“tired” state. As also discussed above, requestor state models 411 mayinclude request type information 403, speech pattern information 405,request history information 407, and/or other suitable factors based onwhich the state of a given requestor 303 may be determined, classified,categorized, clustered, etc. For example, a first requestor state model411 may be associated with an “inquisitive” state, while a secondrequestor state model 411 may be associated with an “angry” state.

As discussed above, APSS 101 may generate, refine, etc. correlationsbetween particular provider state models 211 and requestor state models411. For example, APSS 101 may utilize AI/ML techniques or othersuitable techniques to determine affinities, scores, etc. that reflect ameasure of correlation between a particular provider state model 211 andone or more requestor state models 411. Additionally, or alternatively,APSS 101 may utilize AI/ML techniques or other suitable techniques todetermine affinities, scores, etc. that reflect a measure of correlationbetween a particular requestor state model 411 and one or more providerstate models 211.

Process 700 may further include monitoring (at 704) provider informationassociated with a set of providers 103. For example, as discussed above,APSS 101 may receive biometric and/or sensor information from one ormore UEs 201. UEs 201 may include and/or may be communicatively coupledto sensors or other devices or systems that are suitable to collect someor all of the provider information discussed above. For example, UEs 201may include and/or may be communicatively coupled to an EEG device, anECG device, a pulse monitor, a blood pressure monitor, a camera, and/orsome other suitable device or system.

Process 700 may additionally include determining (at 706) provider statemodels 211 associated with providers 103 based on the monitored providerinformation. For example, as discussed above, provider state models 211may include a set of attributes, characteristics, values, etc. that APSS101 may match to the monitored provider information. Generally, forexample, different provider state models 211 may be associated withdifferent provider states, such as “happy,” “upset,” “tired,” etc.

As indicated in the figure, blocks 704 and/or 706 may be repeatediteratively and/or on an ongoing basis. For example, APSS 101 maymonitor (at 704) provider information on a periodic and/or intermittentbasis. In some embodiments, APSS 101 may “pull” provider information byrequesting or polling one or more UEs 201 or other devices or systems,and/or provider information may be “pushed” to APSS 101 (e.g., withoutand/or independent of requests for such information from APSS 101). Inthis manner, APSS 101 may maintain up-to-date, real time, and/ornear-real time provider state models 211 associated with respectiveproviders 103.

Process 700 may also include identifying (at 708) a service request froma particular requestor 303. For example, APSS 101 may receive anindication from a device or system that receives and/or handles servicerequests from requestors 303. For example, APSS 101 may becommunicatively coupled a web portal, an interactive voice response(“IVR”) system, a call center, an application server, and/or one or moreother devices or systems that receive and/or otherwise handle servicerequests from requestors 303.

Process 700 may further include determining (at 710) requestorinformation associated with requestor 303. For example, as discussedabove, APSS 101 may determine or receive request type information 403,speech pattern information 405, request history information 407 from oneor more UEs 201 associated with requestor 303, and/or may determine suchinformation based on information received from one or more UEs 201. Forexample, a particular UE 201 may provide audio data associated withrequestor 303 (e.g., UE 201 may include a mobile telephone that includesa microphone that captures audible speech), and APSS 101 may analyze theaudio data to determine request type information 403, speech patterninformation 405, and/or other features or derivatives of the audio data.

In some embodiments, APSS 101 may determine (at 710) the requestorinformation in response to identifying (at 708) a service request fromrequestor 303. Additionally, or alternatively, APSS 101 may receiveand/or determine requestor information on some other basis, which may beindependent of when service requests are received from requestor 303.For example, APSS 101 may monitor information associated with requestor303 on an ongoing basis by communicating with one or more UEs 201 orother devices or systems associated with requestor 303.

Process 700 may additionally include determining (at 712) a particularrequestor state model 411 associated with requestor 303 based on therequestor information. For example, as discussed above, APSS 101 mayidentify a particular requestor state model 411 that includesattributes, features, characteristics, etc. that match the requestorinformation. As discussed above, the “matching” requestor state model411 may include identical information as the requestor information. Insome embodiments, the “matching” requestor state model 411 may includeinformation with a measure of similarity (e.g., determined using asuitable similarity analysis) that exceeds a threshold level ofsimilarity, and/or requestor state model 411 may have been selected froma set of candidate requestor state models 411 based on respectivemeasures of similarity between candidate requestor state models 411 andthe requestor information. For example, in some scenarios, the selectedrequestor state model 411 may have a highest measure of similarity tothe requestor information out of the set of requestor state models 411.

Process 700 may also include selecting (at 714) a particular provider103 based on the determined provider state model 211 and requestor statemodel 411. For example, as discussed above, APSS 101 may identify aparticular provider 103 out of a set of candidate providers 103 tohandle the service request. In some embodiments, the set of candidateproviders 103 may include providers 103 that are “available” or someother suitable indicator. For example, a particular provider 103 that iscurrently providing service in response to a service request may not beavailable, while an “idle” provider 103 or provider 103 who is otherwisenot currently providing service in response to a service request may beavailable.

As discussed above, provider 103 may be selected based on the set ofprovider state models 211 (determined at 706) associated with the set ofcandidate providers 103 and the determined (at 712) requestor statemodel 411 associated with requestor 303. Such selection may includeidentifying a particular provider state model 211 that most closelycorrelates to requestor state model 411 and/or one or more otherfactors, as mentioned above.

In some embodiments, APSS 101 may select (at 714) a particular provider103 independent of, or without determining, requestor information orrequestor state model 411. For example, APSS 101 may select a particularprovider 103 based on provider state models 211. As one example, APSS101 may determine that a first provider 103 that is associated with a“tired” state should not handle the service request, and/or maydetermine that a second provider 103 that is associated with a “happy”state should handle the service request.

In some embodiments, APSS 101 may generate one or more scores for eachprovider 103 based on the monitored (at 704) provider information, wheresuch scores reflect the effectiveness, efficacy, or the like ofproviders 103. For example, APSS 101 may generate a relatively highscore for a particular provider 103 whose provider information indicates(e.g., based on identifying that the provider information is associatedwith a particular provider state model 211) that provider 103 is alert,happy, content, and/or otherwise effective in providing services inresponse to service requests. On the other hand, APSS 101 may generate arelatively low score for a provider 103 whose provider informationindicates (e.g., based on a particular provider state model 211determined for provider 103) that provider 103 is tired, unhappy,frustrated, or otherwise less effective in providing services.

Process 700 may further include assigning (at 716) the request to theselected provider 103. For example, APSS 101 may output a notificationto requestor 303 and/or provider 103 that provider 103 has been selectedto handle the service request. In some embodiments, APSS 101 may outputan instruction, notification, or other type of indication to an IVRsystem, web portal, application server, or other device or system. Forexample, APSS 101 may output an indication to a switching, routing, orselection of a call center that a particular call center representativehas been selected to answer a call from a particular caller who isrequesting a service such as an information request, technical support,or the like. Since the selection of the call representative may be basedon the state of the representative (e.g., happy, attentive, tired, etc.)and/or the caller (e.g., surprised, upset, inquisitive, etc.), thesubsequent interactions between the representative and the caller mayhave a higher likelihood of being expedient, convenient, or otherwisepositive than if the states of the representative and/or the caller werenot a factor in the selection.

While examples are provided above in the context of matching a givenrequest or associated requestor 303 to a particular provider 103 (e.g.,based on provider states and/or requestor states), similar concepts mayapply to embodiments that are independent of requests or requestors 303,and/or in which no such requests or requestors 303 exist. For example,APSS 101 may determine (at 706) a provider state model 211 associatedwith a particular provider 103, and may determine one or more actions(e.g., remedial actions or other types of actions) based on thedetermined provider state model 211. For example, APSS 101 may generate,maintain, refine, etc. (e.g., using AI/ML techniques or other suitabletechniques) associations between particular provider state models 211and associated actions. As one example, a given provider state model 211may indicate an “overworked” or “tired” state for provider 103, and maybe associated with an action that includes notifying provider 103 totake a break for a given amount of time.

Similarly, different provider state models 211, associated withdifferent attributes or characteristics (e.g., different biometricand/or other types of information) of provider 103 may be associatedwith different actions, or different parameters for the same action. Forexample, one provider state model 211 may be associated with a first setof provider information (e.g., biometrics and/or other suitableinformation), and may be associated with an action including notifyingprovider 103 to take a break for a first period of time (e.g., onehour), while another provider state model 211 may be associated with asecond set of provider information (e.g., different biometrics ordifferent values for the biometrics than the first set of providerinformation) and may be associated with an action notifying provider 103to take a break for a second period of time (e.g., two hours).

In some embodiments, the actions or remedial actions may includegenerating one or more reports reflecting the state of one or moreproviders 103 over time. Such reports may include graphs, charts,tables, or the like. For example, such reports may include a “happinessover time” graph, a “tiredness over time” graph, or the like, where suchreports are generated based on provider state models 211 determined forproviders 103 over time. In some embodiments, the reports may begenerated for a single provider 103 or a group of providers 103. In thismanner, the states of individuals or groups (e.g., departments,divisions, etc. of an organization) may be readily determined andanalyzed.

In some embodiments, the remedial action may include reassigning a givenprovider 103 to a different department, category, queue, etc. Forexample, a first department to which provider 103 is assigned may handlea first category of requests (e.g., technical support requests), while asecond department may handle a second category of requests (e.g.,general information requests). APSS 101 may have determined that thefirst department and the second department are associated withrespective providers with respective provider states 211, and maydetermine that the second department is a better fit for particularprovider 103. For example, APSS 101 may determine that provider 103 isin a “stressed” state, and may determine that providers associated withthe second department are less likely to be associated with the“stressed” state than providers of the first department. In such ascenario, APSS 101 may output an indication that provider 103 should bereassigned to the second department. Such reassignment may cause thestate of provider 103 to change over time (e.g., to a state other than a“stressed” state).

FIG. 8 illustrates an example environment 800, in which one or moreembodiments may be implemented. In some embodiments, environment 800 maycorrespond to a Fifth Generation (“5G”) network, and/or may includeelements of a 5G network. In some embodiments, environment 800 maycorrespond to a 5G Non-Standalone (“NSA”) architecture, in which a 5Gradio access technology (“RAT”) may be used in conjunction with one ormore other RATs (e.g., a Long-Term Evolution (“LTE”) RAT), and/or inwhich elements of a 5G core network may be implemented by, may becommunicatively coupled with, and/or may include elements of anothertype of core network (e.g., an evolved packet core (“EPC”)). As shown,environment 800 may include UE 201, RAN 810 (which may include one ormore Next Generation Node Bs (“gNBs”) 811), RAN 812 (which may includeone or more one or more evolved Node Bs (“eNBs”) 813), and variousnetwork functions such as Access and Mobility Management Function(“AMF”) 815, Mobility Management Entity (“MME”) 816, Serving Gateway(“SGW”) 817, Session Management Function (“SMF”)/Packet Data Network(“PDN”) Gateway (“PGW”)-Control plane function (“PGW-C”) 820, PolicyControl Function (“PCF”)/Policy Charging and Rules Function (“PCRF”)825, Application Function (“AF”) 830, User Plane Function(“UPF”)/PGW-User plane function (“PGW-U”) 835, Home Subscriber Server(“HSS”)/Unified Data Management (“UDM”) 840, and Authentication ServerFunction (“AUSF”) 845. Environment 800 may also include one or morenetworks, such as Data Network (“DN”) 850. Environment 800 may includeone or more additional devices or systems communicatively coupled to oneor more networks (e.g., DN 850), such as APSS 101.

The example shown in FIG. 8 illustrates one instance of each networkcomponent or function (e.g., one instance of SMF/PGW-C 820, PCF/PCRF825, UPF/PGW-U 835, HSS/UDM 840, and/or 845). In practice, environment800 may include multiple instances of such components or functions. Forexample, in some embodiments, environment 800 may include multiple“slices” of a core network, where each slice includes a discrete set ofnetwork functions (e.g., one slice may include a first instance ofSMF/PGW-C 820, PCF/PCRF 825, UPF/PGW-U 835, HSS/UDM 840, and/or 845,while another slice may include a second instance of SMF/PGW-C 820,PCF/PCRF 825, UPF/PGW-U 835, HSS/UDM 840, and/or 845). The differentslices may provide differentiated levels of service, such as service inaccordance with different Quality of Service (“QoS”) parameters.

The quantity of devices and/or networks, illustrated in FIG. 8 , isprovided for explanatory purposes only. In practice, environment 800 mayinclude additional devices and/or networks, fewer devices and/ornetworks, different devices and/or networks, or differently arrangeddevices and/or networks than illustrated in FIG. 8 . For example, whilenot shown, environment 800 may include devices that facilitate or enablecommunication between various components shown in environment 800, suchas routers, modems, gateways, switches, hubs, etc. Alternatively, oradditionally, one or more of the devices of environment 800 may performone or more network functions described as being performed by anotherone or more of the devices of environment 800. Devices of environment800 may interconnect with each other and/or other devices via wiredconnections, wireless connections, or a combination of wired andwireless connections. In some implementations, one or more devices ofenvironment 800 may be physically integrated in, and/or may bephysically attached to, one or more other devices of environment 800.

UE 201 may include a computation and communication device, such as awireless mobile communication device that is capable of communicatingwith RAN 810, RAN 812, and/or DN 850. UE 201 may be, or may include, aradiotelephone, a personal communications system (“PCS”) terminal (e.g.,a device that combines a cellular radiotelephone with data processingand data communications capabilities), a personal digital assistant(“PDA”) (e.g., a device that may include a radiotelephone, a pager,Internet/intranet access, etc.), a smart phone, a laptop computer, atablet computer, a camera, a personal gaming system, an IoT device(e.g., a sensor, a smart home appliance, or the like), a wearabledevice, an IoT device, a Mobile-to-Mobile (“M2M”) device, or anothertype of mobile computation and communication device. UE 201 may sendtraffic to and/or receive traffic (e.g., user plane traffic) from DN 850via RAN 810, RAN 812, and/or UPF/PGW-U 835.

RAN 810 may be, or may include, a 5G RAN that includes one or more basestations (e.g., one or more gNBs 811), via which UE 201 may communicatewith one or more other elements of environment 800. UE 201 maycommunicate with RAN 810 via an air interface (e.g., as provided by gNB811). For instance, RAN 810 may receive traffic (e.g., voice calltraffic, data traffic, messaging traffic, signaling traffic, etc.) fromUE 201 via the air interface, and may communicate the traffic toUPF/PGW-U 835, and/or one or more other devices or networks. Similarly,RAN 810 may receive traffic intended for UE 201 (e.g., from UPF/PGW-U835, AMF 815, and/or one or more other devices or networks) and maycommunicate the traffic to UE 201 via the air interface.

RAN 812 may be, or may include, a LTE RAN that includes one or more basestations (e.g., one or more eNBs 813), via which UE 201 may communicatewith one or more other elements of environment 800. UE 201 maycommunicate with RAN 812 via an air interface (e.g., as provided by eNB813). For instance, RAN 810 may receive traffic (e.g., voice calltraffic, data traffic, messaging traffic, signaling traffic, etc.) fromUE 201 via the air interface, and may communicate the traffic toUPF/PGW-U 835, and/or one or more other devices or networks. Similarly,RAN 810 may receive traffic intended for UE 201 (e.g., from UPF/PGW-U835, SGW 817, and/or one or more other devices or networks) and maycommunicate the traffic to UE 201 via the air interface.

AMF 815 may include one or more devices, systems, Virtualized NetworkFunctions (“VNFs”), etc., that perform operations to register UE 201with the 5G network, to establish bearer channels associated with asession with UE 201, to hand off UE 201 from the 5G network to anothernetwork, to hand off UE 201 from the other network to the 5G network,manage mobility of UE 201 between RANs 810 and/or gNBs 811, and/or toperform other operations. In some embodiments, the 5G network mayinclude multiple AMFs 815, which communicate with each other via the N14interface (denoted in FIG. 8 by the line marked “N14” originating andterminating at AMF 815).

MME 816 may include one or more devices, systems, VNFs, etc., thatperform operations to register UE 201 with the EPC, to establish bearerchannels associated with a session with UE 201, to hand off UE 201 fromthe EPC to another network, to hand off UE 201 from another network tothe EPC, manage mobility of UE 201 between RANs 812 and/or eNBs 813,and/or to perform other operations.

SGW 817 may include one or more devices, systems, VNFs, etc., thataggregate traffic received from one or more eNBs 813 and send theaggregated traffic to an external network or device via UPF/PGW-U 835.Additionally, SGW 817 may aggregate traffic received from one or moreUPF/PGW-Us 835 and may send the aggregated traffic to one or more eNBs813. SGW 817 may operate as an anchor for the user plane duringinter-eNB handovers and as an anchor for mobility between differenttelecommunication networks or RANs (e.g., RANs 810 and 812).

SMF/PGW-C 820 may include one or more devices, systems, VNFs, etc., thatgather, process, store, and/or provide information in a manner describedherein. SMF/PGW-C 820 may, for example, facilitate in the establishmentof communication sessions on behalf of UE 201. In some embodiments, theestablishment of communications sessions may be performed in accordancewith one or more policies provided by PCF/PCRF 825.

PCF/PCRF 825 may include one or more devices, systems, VNFs, etc., thataggregate information to and from the 5G network and/or other sources.PCF/PCRF 825 may receive information regarding policies and/orsubscriptions from one or more sources, such as subscriber databasesand/or from one or more users (such as, for example, an administratorassociated with PCF/PCRF 825).

AF 830 may include one or more devices, systems, VNFs, etc., thatreceive, store, and/or provide information that may be used indetermining parameters (e.g., quality of service parameters, chargingparameters, or the like) for certain applications.

UPF/PGW-U 835 may include one or more devices, systems, VNFs, etc., thatreceive, store, and/or provide data (e.g., user plane data). Forexample, UPF/PGW-U 835 may receive user plane data (e.g., voice calltraffic, data traffic, etc.), destined for UE 201, from DN 850, and mayforward the user plane data toward UE 201 (e.g., via RAN 810, SMF/PGW-C820, and/or one or more other devices). In some embodiments, multipleUPFs 835 may be deployed (e.g., in different geographical locations),and the delivery of content to UE 201 may be coordinated via the N9interface (e.g., as denoted in FIG. 8 by the line marked “N9”originating and terminating at UPF/PGW-U 835). Similarly, UPF/PGW-U 835may receive traffic from UE 201 (e.g., via RAN 810, SMF/PGW-C 820,and/or one or more other devices), and may forward the traffic toward DN850. In some embodiments, UPF/PGW-U 835 may communicate (e.g., via theN4 interface) with SMF/PGW-C 820, regarding user plane data processed byUPF/PGW-U 835.

HSS/UDM 840 and AUSF 845 may include one or more devices, systems, VNFs,etc., that manage, update, and/or store, in one or more memory devicesassociated with AUSF 845 and/or HSS/UDM 840, profile informationassociated with a subscriber. AUSF 845 and/or HSS/UDM 840 may performauthentication, authorization, and/or accounting operations associatedwith the subscriber and/or a communication session with UE 201.

DN 850 may include one or more wired and/or wireless networks. Forexample, DN 850 may include an Internet Protocol (“IP”)-based PDN, awide area network (“WAN”) such as the Internet, a private enterprisenetwork, and/or one or more other networks. UE 201 may communicate,through DN 850, with data servers, other UEs 201, and/or to otherservers or applications that are coupled to DN 850. DN 850 may beconnected to one or more other networks, such as a public switchedtelephone network (“PSTN”), a public land mobile network (“PLMN”),and/or another network. DN 850 may be connected to one or more devices,such as content providers, applications, web servers, and/or otherdevices, with which UE 201 may communicate.

APSS 101 may include one or more devices, systems, VNFs, etc. thatperform one or more of the operations discussed herein. For example,APSS 101 may receive, generate, and/or refine one or more provider statemodels 211, requestor state models 411, and/or correlations thereof.APSS 101 may monitor and/or otherwise determine information associatedwith one or more providers 103 and/or requestor 303. For example, APSS101 may receive such information from one or more UEs 201 and/or otherdevices or systems that determine, collect, receive, etc. suchinformation. APSS 101 may select a particular provider 103 to respond toa service request from a given requestor 303 based on provider statemodels 211, requestor state models 411, correlations thereof, and/orother suitable information. APSS 101 may output an indication of theselection to one or more UEs 201 or other devices or systems that arecommunicatively coupled to one or more UEs 201 associated with provider103 and/or requestor 303.

FIG. 9 illustrates an example Distributed Unit (“DU”) network 900, whichmay be included in and/or implemented by one or more RANs (e.g., RAN810, RAN 812, or some other RAN). In some embodiments, a particular RANmay include one DU network 900. In some embodiments, a particular RANmay include multiple DU networks 900. In some embodiments, DU network900 may correspond to a particular gNB 811 of a 5G RAN (e.g., RAN 810).In some embodiments, DU network 900 may correspond to multiple gNBs 811.In some embodiments, DU network 900 may correspond to one or more othertypes of base stations of one or more other types of RANs. As shown, DUnetwork 900 may include Central Unit (“CU”) 905, one or more DistributedUnits (“DUs”) 903-1 through 903-N (referred to individually as “DU 903,”or collectively as “DUs 903”), and one or more Radio Units (“RUs”) 901-1through 901-M (referred to individually as “RU 901,” or collectively as“RUs 901”).

CU 905 may communicate with a core of a wireless network (e.g., maycommunicate with one or more of the devices or systems described abovewith respect to FIG. 8 , such as AMF 815 and/or UPF/PGW-U 835). In theuplink direction (e.g., for traffic from UEs 201 to a core network), CU905 may aggregate traffic from DUs 903, and forward the aggregatedtraffic to the core network. In some embodiments, CU 905 may receivetraffic according to a given protocol (e.g., Radio Link Control (“RLC”))from DUs 903, and may perform higher-layer processing (e.g., mayaggregate/process RLC packets and generate Packet Data ConvergenceProtocol (“PDCP”) packets based on the RLC packets) on the trafficreceived from DUs 903.

In accordance with some embodiments, CU 905 may receive downlink traffic(e.g., traffic from the core network) for a particular UE 201, and maydetermine which DU(s) 903 should receive the downlink traffic. DU 903may include one or more devices that transmit traffic between a corenetwork (e.g., via CU 905) and UE 201 (e.g., via a respective RU 901).DU 903 may, for example, receive traffic from RU 901 at a first layer(e.g., physical (“PHY”) layer traffic, or lower PHY layer traffic), andmay process/aggregate the traffic to a second layer (e.g., upper PHYand/or RLC). DU 903 may receive traffic from CU 905 at the second layer,may process the traffic to the first layer, and provide the processedtraffic to a respective RU 901 for transmission to UE 201.

RU 901 may include hardware circuitry (e.g., one or more RFtransceivers, antennas, radios, and/or other suitable hardware) tocommunicate wirelessly (e.g., via an RF interface) with one or more UEs201, one or more other DUs 903 (e.g., via RUs 901 associated with DUs903), and/or any other suitable type of device. In the uplink direction,RU 901 may receive traffic from UE 201 and/or another DU 903 via the RFinterface and may provide the traffic to DU 903. In the downlinkdirection, RU 901 may receive traffic from DU 903, and may provide thetraffic to UE 201 and/or another DU 903.

RUs 901 may, in some embodiments, be communicatively coupled to one ormore Multi-Access/Mobile Edge Computing (“MEC”) devices, referred tosometimes herein simply as (“MECs”) 907. For example, RU 901-1 may becommunicatively coupled to MEC 907-1, RU 901-M may be communicativelycoupled to MEC 907-M, DU 903-1 may be communicatively coupled to MEC907-2, DU 903-N may be communicatively coupled to MEC 907-N, CU 905 maybe communicatively coupled to MEC 907-3, and so on. MECs 907 may includehardware resources (e.g., configurable or provisionable hardwareresources) that may be configured to provide services and/or otherwiseprocess traffic to and/or from UE 201, via a respective RU 901.

For example, RU 901-1 may route some traffic, from UE 201, to MEC 907-1instead of to a core network (e.g., via DU 903 and CU 905). MEC 907-1may process the traffic, perform one or more computations based on thereceived traffic, and may provide traffic to UE 201 via RU 901-1. Inthis manner, ultra-low latency services may be provided to UE 201, astraffic does not need to traverse DU 903, CU 905, and an interveningbackhaul network between DU network 900 and the core network. In someembodiments, MEC 907 may include, and/or may implement some or all ofthe functionality described above with respect to APSS 101.

FIG. 10 illustrates example components of device 1000. One or more ofthe devices described above may include one or more devices 1000. Device1000 may include bus 1010, processor 1020, memory 1030, input component1040, output component 1050, and communication interface 1060. Inanother implementation, device 1000 may include additional, fewer,different, or differently arranged components.

Bus 1010 may include one or more communication paths that permitcommunication among the components of device 1000. Processor 1020 mayinclude a processor, microprocessor, or processing logic that mayinterpret and execute instructions. Memory 1030 may include any type ofdynamic storage device that may store information and instructions forexecution by processor 1020, and/or any type of non-volatile storagedevice that may store information for use by processor 1020.

Input component 1040 may include a mechanism that permits an operator toinput information to device 1000 and/or other receives or detects inputfrom a source external to 1040, such as a touchpad, a touchscreen, akeyboard, a keypad, a button, a switch, a microphone or other audioinput component, etc. In some embodiments, input component 1040 mayinclude, or may be communicatively coupled to, one or more sensors, suchas a motion sensor (e.g., which may be or may include a gyroscope,accelerometer, or the like), a location sensor (e.g., a GlobalPositioning System (“GPS”)-based location sensor or some other suitabletype of location sensor or location determination component), athermometer, a barometer, a biometric sensor (e.g., a fingerprintsensor, an EEG monitor, an ECG monitor, a blood pressure sensor, a pulseand/or heartbeat sensor, or the like), and/or some other type of sensor.Output component 1050 may include a mechanism that outputs informationto the operator, such as a display, a speaker, one or more lightemitting diodes (“LEDs”), etc.

Communication interface 1060 may include any transceiver-like mechanismthat enables device 1000 to communicate with other devices and/orsystems. For example, communication interface 1060 may include anEthernet interface, an optical interface, a coaxial interface, or thelike. Communication interface 1060 may include a wireless communicationdevice, such as an infrared (“IR”) receiver, a Bluetooth® radio, or thelike. The wireless communication device may be coupled to an externaldevice, such as a remote control, a wireless keyboard, a mobiletelephone, etc. In some embodiments, device 1000 may include more thanone communication interface 1060. For instance, device 1000 may includean optical interface and an Ethernet interface.

Device 1000 may perform certain operations relating to one or moreprocesses described above. Device 1000 may perform these operations inresponse to processor 1020 executing software instructions stored in acomputer-readable medium, such as memory 1030. A computer-readablemedium may be defined as a non-transitory memory device. A memory devicemay include space within a single physical memory device or spreadacross multiple physical memory devices. The software instructions maybe read into memory 1030 from another computer-readable medium or fromanother device. The software instructions stored in memory 1030 maycause processor 1020 to perform processes described herein.Alternatively, hardwired circuitry may be used in place of or incombination with software instructions to implement processes describedherein. Thus, implementations described herein are not limited to anyspecific combination of hardware circuitry and software.

The foregoing description of implementations provides illustration anddescription, but is not intended to be exhaustive or to limit thepossible implementations to the precise form disclosed. Modificationsand variations are possible in light of the above disclosure or may beacquired from practice of the implementations.

For example, while series of blocks and/or signals have been describedabove (e.g., with regard to FIGS. 1-7 ), the order of the blocks and/orsignals may be modified in other implementations. Further, non-dependentblocks and/or signals may be performed in parallel. Additionally, whilethe figures have been described in the context of particular devicesperforming particular acts, in practice, one or more other devices mayperform some or all of these acts in lieu of, or in addition to, theabove-mentioned devices.

The actual software code or specialized control hardware used toimplement an embodiment is not limiting of the embodiment. Thus, theoperation and behavior of the embodiment has been described withoutreference to the specific software code, it being understood thatsoftware and control hardware may be designed based on the descriptionherein.

In the preceding specification, various example embodiments have beendescribed with reference to the accompanying drawings. It will, however,be evident that various modifications and changes may be made thereto,and additional embodiments may be implemented, without departing fromthe broader scope of the invention as set forth in the claims thatfollow. The specification and drawings are accordingly to be regarded inan illustrative rather than restrictive sense.

Even though particular combinations of features are recited in theclaims and/or disclosed in the specification, these combinations are notintended to limit the disclosure of the possible implementations. Infact, many of these features may be combined in ways not specificallyrecited in the claims and/or disclosed in the specification. Althougheach dependent claim listed below may directly depend on only one otherclaim, the disclosure of the possible implementations includes eachdependent claim in combination with every other claim in the claim set.

Further, while certain connections or devices are shown, in practice,additional, fewer, or different, connections or devices may be used.Furthermore, while various devices and networks are shown separately, inpractice, the functionality of multiple devices may be performed by asingle device, or the functionality of one device may be performed bymultiple devices. Further, multiple ones of the illustrated networks maybe included in a single network, or a particular network may includemultiple networks. Further, while some devices are shown ascommunicating with a network, some such devices may be incorporated, inwhole or in part, as a part of the network.

To the extent the aforementioned implementations collect, store, oremploy personal information of individuals, groups or other entities, itshould be understood that such information shall be used in accordancewith all applicable laws concerning protection of personal information.Additionally, the collection, storage, and use of such information canbe subject to consent of the individual to such activity, for example,through well known “opt-in” or “opt-out” processes as can be appropriatefor the situation and type of information. Storage and use of personalinformation can be in an appropriately secure manner reflective of thetype of information, for example, through various access control,encryption and anonymization techniques for particularly sensitiveinformation.

No element, act, or instruction used in the present application shouldbe construed as critical or essential unless explicitly described assuch. An instance of the use of the term “and,” as used herein, does notnecessarily preclude the interpretation that the phrase “and/or” wasintended in that instance. Similarly, an instance of the use of the term“or,” as used herein, does not necessarily preclude the interpretationthat the phrase “and/or” was intended in that instance. Also, as usedherein, the article “a” is intended to include one or more items, andmay be used interchangeably with the phrase “one or more.” Where onlyone item is intended, the terms “one,” “single,” “only,” or similarlanguage is used. Further, the phrase “based on” is intended to mean“based, at least in part, on” unless explicitly stated otherwise.

What is claimed is:
 1. A device, comprising: one or more processorsconfigured to: maintain a first set of artificial intelligence/machinelearning (“AI/ML”) models that correlate respective sets of brain waveinformation to respective provider states; measure brain waveinformation associated with a plurality of service providers via one ormore Electroencephalography (“EEG”) devices; determine a particularprovider state associated with each respective service provider, of theplurality of service providers, based on the measured brain waveinformation associated with each respective service provider, whereindetermining the provider state for each service provider includes:comparing the measured brain wave information, associated with eachservice provider, to the first set of AI/ML models, identifying, foreach service provider and based on the comparing, a respective AI/MLmodel that corresponds to the respective brain wave informationassociated with the each service provider, and identifying a particularprovider state, for each service provider, based on the identified AI/MLmodel for the each service provider; maintain a plurality of servicequeues that are each associated with a respective type of servicerequest; maintain a second set of AI/ML models that correlate respectiveprovider states to respective service queues of the plurality of servicequeues; assign particular service providers to respective servicequeues, of the plurality of service queues, based on comparingrespective provider states of the plurality of providers to the secondset of AI/ML models; receive an indication of a service request;identify a particular type of the service request; select, based on thedetermined provider states, a particular service provider, of theplurality of service providers, to respond to the service request,wherein selecting the particular service provider includes: selecting aparticular service queue, out of the plurality of service queues, thatare associated with the particular type of the service request, andselecting the particular service provider from the service providersassigned to the particular service queue; and respond to the servicerequest by providing an indication of the selected particular serviceprovider.
 2. The device of claim 1, wherein selecting the particularservice provider includes generating a score for each service provider,of the plurality of service providers, wherein the generated scores arebased on respective biometric information associated with each serviceprovider.
 3. The device of claim 1, wherein the one or more processorsare further configured to: determine a particular requestor stateassociated with the service request; and determine a respective measureof correlation between each provider state, of the determined providerstates, and the particular requestor state, wherein selecting theparticular service provider is based on the determined respectivemeasures of correlation between the determined provider states and theparticular requestor state.
 4. The device of claim 1, whereindetermining the particular provider state associated with eachparticular service provider, of the plurality of service providers,further includes at least one of: determining speech pattern informationassociated with the one or more service providers, determining pulseinformation associated with the one or more service providers, ordetermining blood pressure information associated with the one or moreservice providers.
 5. The device of claim 1, wherein the particularservice provider is a first service provider, wherein the one or moreprocessors are further configured to: identify that a second serviceprovider, of the plurality of service providers, is associated with aparticular provider state; and output an alert that the particularservice provider is associated with the particular provider state. 6.The device of claim 1, wherein the particular service provider is afirst service provider, wherein the one or more processors are furtherconfigured to: identify that a second service provider, of the pluralityof service providers, is associated with a particular provider state;and reassign, based on identifying that the second service provider isassociated with the particular provider state, from a first servicequeue associated with a first type of service request to a second queueassociated with a second type of service request.
 7. The device of claim1, wherein determining the particular provider state associated witheach respective service provider, of the plurality of service providers,includes: monitoring first biometric information associated with a firstservice provider of the plurality of service providers over a firstperiod of time, and monitoring second biometric information associatedwith the first service provider over a second period of time that issubsequent to the first period of time; wherein the one or moreprocessors are further configured to: determine that the secondbiometric information differs from the first biometric information; andupdate, based on determining that the second biometric informationdiffers from the first biometric information, provider state informationassociated with the first service provider from a first provider state,associated with the first period of time, to a second provider state,associated with the second period of time.
 8. A non-transitorycomputer-readable medium, storing a plurality of processor-executableinstructions to: maintain a first set of artificial intelligence/machinelearning (“AI/ML”) models that correlate respective sets of brain waveinformation to respective provider states; measure brain waveinformation associated with a plurality of service providers via one ormore Electroencephalography (“EEG”) devices; determine a particularprovider state associated with each respective service provider, of theplurality of service providers, based on the measured brain waveinformation associated with each respective service provider, whereindetermining the provider state for each service provider includes:comparing the measured brain wave information, associated with eachservice provider, to the first set of AI/ML models, identifying, foreach service provider and based on the comparing, a respective AI/MLmodel that corresponds to the respective brain wave informationassociated with the each service provider, and identifying a particularprovider state, for each service provider, based on the identified AI/MLmodel for the each service provider; maintain a plurality of servicequeues that are each associated with a respective type of servicerequest; maintain a second set of AI/ML models that correlate respectiveprovider states to respective service queues of the plurality of servicequeues; assign particular service providers to respective servicequeues, of the plurality of service queues, based on comparingrespective provider states of the plurality of providers to the secondset of AI/ML models; receive an indication of a service request;identify a particular type of the service request; select, based on thedetermined provider states, a particular service provider, of theplurality of service providers, to respond to the service request,wherein selecting the particular service provider includes: selecting aparticular service queue, out of the plurality of service queues, thatare associated with the particular type of the service request, andselecting the particular service provider from the service providersassigned to the particular service queue; and respond to the servicerequest by providing an indication of the selected particular serviceprovider.
 9. The non-transitory computer-readable medium of claim 8,wherein selecting the particular service provider includes generating ascore for each service provider, of the plurality of service providers,wherein the generated scores are based on respective biometricinformation associated with each service provider.
 10. Thenon-transitory computer-readable medium of claim 8, wherein theplurality of processor-executable instructions further includeprocessor-executable instructions to: determine a particular requestorstate associated with the service request; and determine a respectivemeasure of correlation between each provider state, of the determinedprovider states, and the particular requestor state, wherein selectingthe particular service provider is based on the determined respectivemeasures of correlation between the determined provider states and theparticular requestor state.
 11. The non-transitory computer-readablemedium of claim 8, wherein determining the particular provider stateassociated with each particular service provider, of the plurality ofservice providers, further includes at least one of: determining speechpattern information associated with the one or more service providers,determining pulse information associated with the one or more serviceproviders, or determining blood pressure information associated with theone or more service providers.
 12. The non-transitory computer-readablemedium of claim 8, wherein the particular service provider is a firstservice provider, wherein the plurality of processor-executableinstructions further include processor-executable instructions to:identify that a second service provider, of the plurality of serviceproviders, is associated with a particular provider state; and output analert that the particular service provider is associated with theparticular provider state.
 13. The non-transitory computer-readablemedium of claim 8, wherein the particular service provider is a firstservice provider, wherein the plurality of processor-executableinstructions further include processor-executable instructions to:identify that a second service provider, of the plurality of serviceproviders, is associated with a particular provider state; and reassign,based on identifying that the second service provider is associated withthe particular provider state, from a first service queue associatedwith a first type of service request to a second queue associated with asecond type of service request.
 14. The non-transitory computer-readablemedium of claim 8, wherein determining the particular provider stateassociated with each respective service provider, of the plurality ofservice providers, includes: monitoring first biometric informationassociated with a first service provider of the plurality of serviceproviders over a first period of time, and monitoring second biometricinformation associated with the first service provider over a secondperiod of time that is subsequent to the first period of time; whereinthe plurality of processor-executable instructions further includeprocessor-executable instructions to: determine that the secondbiometric information differs from the first biometric information; andupdate, based on determining that the second biometric informationdiffers from the first biometric information, provider state informationassociated with the first service provider from a first provider state,associated with the first period of time, to a second provider state,associated with the second period of time.
 15. A method, comprising:maintaining a first set of artificial intelligence/machine learning(“AI/ML”) models that correlate respective sets of brain waveinformation to respective provider states; measuring brain waveinformation associated with a plurality of service providers via one ormore Electroencephalography (“EEG”) devices; determining a particularprovider state associated with each respective service provider, of theplurality of service providers, based on the measured brain waveinformation associated with each respective service provider, whereindetermining the provider state for each service provider includes:comparing the measured brain wave information, associated with eachservice provider, to the first set of AI/ML models, identifying, foreach service provider and based on the comparing, a respective AI/MLmodel that corresponds to the respective brain wave informationassociated with the each service provider, and identifying a particularprovider state, for each service provider, based on the identified AI/MLmodel for the each service provider; maintaining a plurality of servicequeues that are each associated with a respective type of servicerequest; maintaining a second set of AI/ML models that correlaterespective provider states to respective service queues of the pluralityof service queues; assigning particular service providers to respectiveservice queues, of the plurality of service queues, based on comparingrespective provider states of the plurality of providers to the secondset of AI/ML models; receiving an indication of a service request;identifying a particular type of the service request; selecting, basedon the determined provider states, a particular service provider, of theplurality of service providers, to respond to the service request,wherein selecting the particular service provider includes: selecting aparticular service queue, out of the plurality of service queues, thatare associated with the particular type of the service request, andselecting the particular service provider from the service providersassigned to the particular service queue; and responding to the servicerequest by providing an indication of the selected particular serviceprovider.
 16. The method of claim 15, wherein selecting the particularservice provider includes generating a score for each service provider,of the plurality of service providers, wherein the generated scores arebased on respective biometric information associated with each serviceprovider.
 17. The method of claim 15, the method further comprising:determining a particular requestor state associated with the servicerequest; and determining a respective measure of correlation betweeneach provider state, of the determined provider states, and theparticular requestor state, wherein selecting the particular serviceprovider is based on the determined respective measures of correlationbetween the determined provider states and the particular requestorstate.
 18. The method of claim 15, wherein determining the particularprovider state associated with each particular service provider, of theplurality of service providers, further includes at least one of:determining speech pattern information associated with the one or moreservice providers, determining pulse information associated with the oneor more service providers, or determining blood pressure informationassociated with the one or more service providers.
 19. The method ofclaim 15, wherein the particular service provider is a first serviceprovider, the method further comprising: identifying that a secondservice provider, of the plurality of service providers, is associatedwith a particular provider state; and outputting an alert that theparticular service provider is associated with the particular providerstate.
 20. The method of claim 15, wherein the particular serviceprovider is a first service provider, the method further comprising:identifying that a second service provider, of the plurality of serviceproviders, is associated with a particular provider state; andreassigning, based on identifying that the second service provider isassociated with the particular provider state, from a first servicequeue associated with a first type of service request to a second queueassociated with a second type of service request.